Affirmitive Action, passed on September 24,1965, refers to policies and measures implemented by governments, organizations, or institutions to address historical or ongoing discrimination and promote equal opportunities for underrepresented groups, typically in the areas of employment, education, and public contracting. On June 29, 2023 the Supreme Court ruled on affirmitive action and called for the end of race-conscios admission practices in higher education.
This report examines the effects of affirmative action on the racial and ethnic demographics of Stanford University from 1980 to 2021. California abolished affirmative action for higher education in 1996 with the passage of Proposition 209. This report focuses on analyzing the effects of the absence of affirmative action on Stanford University, delving into the changes in racial and ethnic composition over time. Through exploratory data analysis the following questions will be addressed:
By analyzing historical data and employing statistical techniques, this report aims to provide insights into the impact of affirmative action on Stanford's demographics, contributing to a broader understanding of the educational system's role in promoting diversity and equal opportunities for historically marginalized groups.
# Import the Necessary Libraries
import pandas as pd
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
The concept of race and ethinicity classification is ever changing. As our society and population has changed so has the methods and language used to classify race and ethinicity in the United States. For accuracy and consistency throughout this report the data pulled from the Nationcal Center of Eduation Statistics was divided into two timeframes, 1980-2007 and 2010-2021.
Before 2010, racial groups such as Asian and Pascific Islanders were lumped together into one category where as in recent studies Asian classifies as one group and Native Hawaiian or Other Pacific Islander is classified as a completely different group. Other changes such as the addition of more categories such as a category for people who identify with two or more races was also added in more recent studies following 2010.
# Import the .csv File to Be Read
df_demographics = pd.read_csv('Stanford Demographics 1980-2007.csv')
pd.set_option('display.max_columns',None)
# Rename Column Headers for Clarity
df_demographics = df_demographics.rename(columns={'year':'Year',
'EF1980_A.Grand total': 'Grand Total',
'EF1980_A.Total men, reported':'Grand Total Men',
'EF1980_A.Total women, reported':'Grand Total Women',
'EF1980_A.White non-Hispanic total':'White Non-Hispanic Total',
'EF1980_A.White non-Hispanic men':'White Non-Hispanic Men',
'EF1980_A.White non-Hispanic women':'White Non-Hispanic Women',
'EF1980_A.Black non-Hispanic total':'Black Non-Hispanic Total',
'EF1980_A.Black non-Hispanic men':'Black Non-Hispanic Men',
'EF1980_A.Black non-Hispanic women':'Black Non-Hispanic Women',
'EF1980_A.Hispanic total':'Hispanic Total',
'EF1980_A.Hispanic men':'Hispanic Men',
'EF1980_A.Hispanic women':'Hispanic Women',
'EF1980_A.Asian or Pacific Islander total':'Asian or Pacific Islander Total',
'EF1980_A.Asian or Pacific Islander men':'Asian or Pacific Islander Men',
'EF1980_A.Asian or Paciific Islander women':'Asian or Pacific Islander Women',
'EF1980_A.American Indian or Alaska Native total':'American Indian or Alaska Native Total',
'EF1980_A.American Indian or Alaska native men':'American Indian or Alaska Native Men',
'EF1980_A.American Indian or Alaska native women':'American Indian or Alaska Native Women',
'ef1991_a.Race/ethnicity unknown total':'Race/Ethnicity Unknown Total',
'EF1992_A.Race/ethnicity unknown men':'Race/Ethnicity Unknown Men',
'EF1992_A.Race/ethnicity unknown women':'Race/Ethnicity Unknown Women',
'EF1980_A.Nonresident alien total':'Nonresident Alien Total',
'EF1980_A.Nonresident alien men':'Nonresident Alien Total Men',
'EF1980_A.Nonresident alien women':'Nonresident Alien Total Women'})
# Drop Irrelevant Data from the Data Frame
df_demographics= df_demographics.drop('unitid',axis=1)
df_demographics = df_demographics.drop(range(1,40,2))
df_demographics = df_demographics.drop('Unnamed: 28', axis=1)
df_demographics = df_demographics.drop('EF1980_A.Level of student', axis=1)
df_demographics = df_demographics.drop('institution name', axis=1)
df_demographics = df_demographics.reset_index(drop=True) # Reset Index
df_demographics
| Year | Grand Total | Grand Total Men | Grand Total Women | White Non-Hispanic Total | White Non-Hispanic Men | White Non-Hispanic Women | Black Non-Hispanic Total | Black Non-Hispanic Men | Black Non-Hispanic Women | Hispanic Total | Hispanic Men | Hispanic Women | Asian or Pacific Islander Total | Asian or Pacific Islander Men | Asian or Pacific Islander Women | American Indian or Alaska Native Total | American Indian or Alaska Native Men | American Indian or Alaska Native Women | Race/Ethnicity Unknown Total | Race/Ethnicity Unknown Men | Race/Ethnicity Unknown Women | Nonresident Alien Total | Nonresident Alien Total Men | Nonresident Alien Total Women | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1980 | 6668 | 3768 | 2900 | 5224 | 2986 | 2238 | 405 | 208 | 197 | 434 | 257 | 177 | 418 | 210 | 208 | 38 | 20 | 18 | NaN | NaN | NaN | 149 | 87 | 62 |
| 1 | 1984 | 6634 | 3800 | 2834 | 4702 | 2739 | 1963 | 507 | 256 | 251 | 575 | 322 | 253 | 491 | 259 | 232 | 39 | 22 | 17 | NaN | NaN | NaN | 320 | 202 | 118 |
| 2 | 1986 | 6614 | 3751 | 2863 | 4508 | 2651 | 1857 | 511 | 241 | 270 | 624 | 345 | 279 | 645 | 314 | 331 | 46 | 25 | 21 | NaN | NaN | NaN | 280 | 175 | 105 |
| 3 | 1988 | 6506 | 3668 | 2838 | 4200 | 2458 | 1742 | 547 | 275 | 272 | 614 | 333 | 281 | 885 | 440 | 445 | 56 | 27 | 29 | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 1991 | 6640 | 3621 | 3019 | 3828 | 2206 | 1622 | 525 | 233 | 292 | 655 | 346 | 309 | 1324 | 647 | 677 | 79 | 33 | 46 | 53 | NaN | NaN | 229 | 156 | 73 |
| 5 | 1992 | 6816 | 3631 | 3185 | 3773 | 2131 | 1642 | 512 | 208 | 304 | 701 | 362 | 339 | 1485 | 718 | 767 | 82 | 30 | 52 | 37 | 22 | 15 | 263 | 182 | 81 |
| 6 | 1993 | 7007 | 3662 | 3345 | 3760 | 2054 | 1706 | 514 | 219 | 295 | 741 | 387 | 354 | 1617 | 784 | 833 | 92 | 37 | 55 | 50 | 29 | 21 | 283 | 181 | 102 |
| 7 | 1994 | 7075 | 3681 | 3394 | 3567 | 1929 | 1638 | 520 | 212 | 308 | 727 | 366 | 361 | 1646 | 792 | 854 | 91 | 37 | 54 | 210 | 149 | 61 | 314 | 196 | 118 |
| 8 | 1995 | 6946 | 3430 | 3516 | 3562 | 1826 | 1736 | 531 | 218 | 313 | 775 | 368 | 407 | 1581 | 751 | 830 | 99 | 40 | 59 | 84 | 38 | 46 | 314 | 189 | 125 |
| 9 | 1996 | 6814 | 3345 | 3469 | 3546 | 1771 | 1775 | 506 | 216 | 290 | 732 | 363 | 369 | 1545 | 736 | 809 | 96 | 37 | 59 | 85 | 43 | 42 | 304 | 179 | 125 |
| 10 | 1997 | 7127 | 3510 | 3617 | 3755 | 1856 | 1899 | 524 | 230 | 294 | 719 | 356 | 363 | 1588 | 752 | 836 | 86 | 36 | 50 | 144 | 91 | 53 | 311 | 189 | 122 |
| 11 | 1998 | 7146 | 3489 | 3657 | 3821 | 1890 | 1931 | 528 | 233 | 295 | 722 | 369 | 353 | 1543 | 723 | 820 | 80 | 30 | 50 | 147 | 62 | 85 | 305 | 182 | 123 |
| 12 | 1999 | 7784 | 3797 | 3987 | 3729 | 1908 | 1821 | 554 | 251 | 303 | 710 | 359 | 351 | 1593 | 726 | 867 | 85 | 33 | 52 | 802 | 324 | 478 | 311 | 196 | 115 |
| 13 | 2000 | 7886 | 3779 | 4107 | 3750 | 1889 | 1861 | 578 | 261 | 317 | 682 | 328 | 354 | 1656 | 751 | 905 | 103 | 45 | 58 | 798 | 316 | 482 | 319 | 189 | 130 |
| 14 | 2001 | 7279 | 3597 | 3682 | 3701 | 1873 | 1828 | 572 | 265 | 307 | 718 | 348 | 370 | 1690 | 803 | 887 | 112 | 45 | 67 | 152 | 73 | 79 | 334 | 190 | 144 |
| 15 | 2002 | 7360 | 3610 | 3750 | 3567 | 1806 | 1761 | 649 | 304 | 345 | 772 | 361 | 411 | 1686 | 777 | 909 | 122 | 53 | 69 | 220 | 105 | 115 | 344 | 204 | 140 |
| 16 | 2003 | 7054 | 3520 | 3534 | 3105 | 1580 | 1525 | 682 | 315 | 367 | 807 | 374 | 433 | 1666 | 811 | 855 | 125 | 56 | 69 | 296 | 149 | 147 | 373 | 235 | 138 |
| 17 | 2004 | 6555 | 3430 | 3125 | 2693 | 1459 | 1234 | 698 | 335 | 363 | 768 | 369 | 399 | 1581 | 791 | 790 | 137 | 58 | 79 | 293 | 156 | 137 | 385 | 262 | 123 |
| 18 | 2005 | 6576 | 3463 | 3113 | 2678 | 1463 | 1215 | 688 | 339 | 349 | 739 | 360 | 379 | 1584 | 788 | 796 | 141 | 64 | 77 | 345 | 186 | 159 | 401 | 263 | 138 |
| 19 | 2006 | 6422 | 3338 | 3084 | 2590 | 1366 | 1224 | 661 | 333 | 328 | 727 | 355 | 372 | 1558 | 774 | 784 | 150 | 62 | 88 | 343 | 195 | 148 | 393 | 253 | 140 |
| 20 | 2007 | 6584 | 3392 | 3192 | 2721 | 1442 | 1279 | 623 | 310 | 313 | 757 | 385 | 372 | 1583 | 766 | 817 | 149 | 68 | 81 | 336 | 173 | 163 | 415 | 248 | 167 |
# Import the .csv File to Be Read
df_demographics2 = pd.read_csv('Stanford Demographics 2010-2021.csv')
pd.set_option('display.max_columns',None)
# Drop Irrelevant Data from the Data Frame
df_demographics2= df_demographics2.drop('unitid',axis=1)
df_demographics2 = df_demographics2.drop([0,2,4,6,8,10,12,14,16,18,20,22])
df_demographics2 = df_demographics2.drop('IDX_EF', axis=1)
df_demographics2 = df_demographics2.drop('Level of Student', axis=1)
df_demographics2 = df_demographics2.drop('Institution Name', axis=1)
df_demographics2 = df_demographics2.reset_index(drop=True) # Reset Index
#Check Data for Inconsisitencies
df_demographics2.duplicated().sum()
df_demographics2.isnull().sum()
# Further Information about the DataFrame
#df_demographics.info()
df_demographics2
| Year | Grand Total | Grand Total Men | Grand Total Women | American Indian or Alaska Native Total | American Indian or Alaska Native Men | American Indian or Alaska Native Women | Asian Total | Asian Men | Asian Women | Black or African American Total | Black or African American Men | Black or African American Women | Hispanic Total | Hispanic Men | Hispanic Women | Native Hawaiian or Other Pacific Islander Total | Native Hawaiian or Other Pacific Islander Men | Native Hawaiian or Other Pacific Islander Women | White Total | White Men | White Women | Two or More Races Total | Two or More Races Men | Two or More Races Women | Race/Ethnicity Unknown Total | Race/Ethnicity Unknown Men | Race/Ethnicity Unknown Women | Nonresident Alien Total | Nonresident Alien Men | Nonresident Alien Women | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2010 | 6940 | 3572 | 3368 | 78 | 36 | 42 | 1236 | 588 | 648 | 485 | 237 | 248 | 1081 | 566 | 515 | 29 | 14 | 15 | 2702 | 1451 | 1251 | 723 | 343 | 380 | 93 | 52 | 41 | 513 | 285 | 228 |
| 1 | 2011 | 6988 | 3608 | 3380 | 65 | 36 | 29 | 1260 | 622 | 638 | 509 | 249 | 260 | 1151 | 594 | 557 | 32 | 16 | 16 | 2562 | 1357 | 1205 | 802 | 375 | 427 | 80 | 47 | 33 | 527 | 312 | 215 |
| 2 | 2012 | 7063 | 3674 | 3389 | 69 | 40 | 29 | 1331 | 682 | 649 | 441 | 217 | 224 | 1179 | 632 | 547 | 26 | 13 | 13 | 2667 | 1398 | 1269 | 754 | 350 | 404 | 55 | 36 | 19 | 541 | 306 | 235 |
| 3 | 2013 | 7274 | 3743 | 3531 | 63 | 33 | 30 | 1340 | 700 | 640 | 444 | 231 | 213 | 1178 | 647 | 531 | 26 | 12 | 14 | 2660 | 1379 | 1281 | 753 | 354 | 399 | 245 | 37 | 208 | 565 | 350 | 215 |
| 4 | 2014 | 7019 | 3704 | 3315 | 80 | 41 | 39 | 1389 | 704 | 685 | 415 | 217 | 198 | 1128 | 641 | 487 | 27 | 13 | 14 | 2633 | 1340 | 1293 | 749 | 363 | 386 | 23 | 15 | 8 | 575 | 370 | 205 |
| 5 | 2015 | 7000 | 3667 | 3333 | 71 | 35 | 36 | 1432 | 724 | 708 | 425 | 212 | 213 | 1072 | 608 | 464 | 22 | 13 | 9 | 2614 | 1321 | 1293 | 727 | 348 | 379 | 24 | 17 | 7 | 613 | 389 | 224 |
| 6 | 2016 | 7034 | 3621 | 3413 | 75 | 40 | 35 | 1454 | 684 | 770 | 449 | 219 | 230 | 1117 | 630 | 487 | 24 | 13 | 11 | 2545 | 1304 | 1241 | 700 | 328 | 372 | 28 | 16 | 12 | 642 | 387 | 255 |
| 7 | 2017 | 7064 | 3513 | 3551 | 63 | 29 | 34 | 1529 | 684 | 845 | 471 | 218 | 253 | 1102 | 597 | 505 | 22 | 13 | 9 | 2518 | 1277 | 1241 | 679 | 310 | 369 | 31 | 19 | 12 | 649 | 366 | 283 |
| 8 | 2018 | 7087 | 3537 | 3550 | 56 | 26 | 30 | 1552 | 717 | 835 | 462 | 213 | 249 | 1113 | 583 | 530 | 22 | 14 | 8 | 2415 | 1244 | 1171 | 673 | 304 | 369 | 55 | 31 | 24 | 739 | 405 | 334 |
| 9 | 2019 | 6996 | 3468 | 3528 | 57 | 26 | 31 | 1590 | 736 | 854 | 477 | 228 | 249 | 1156 | 594 | 562 | 25 | 14 | 11 | 2264 | 1162 | 1102 | 653 | 293 | 360 | 26 | 17 | 9 | 748 | 398 | 350 |
| 10 | 2020 | 6366 | 3124 | 3242 | 62 | 27 | 35 | 1579 | 752 | 827 | 473 | 214 | 259 | 1082 | 547 | 535 | 22 | 14 | 8 | 1846 | 943 | 903 | 609 | 275 | 334 | 14 | 11 | 3 | 679 | 341 | 338 |
| 11 | 2021 | 7645 | 3778 | 3867 | 67 | 29 | 38 | 1917 | 951 | 966 | 547 | 260 | 287 | 1339 | 677 | 662 | 21 | 10 | 11 | 2139 | 1084 | 1055 | 778 | 338 | 440 | 19 | 15 | 4 | 818 | 414 | 404 |
# Analysis of Gender Breakdown from 1980 - 2007
fig_gender_bar = px.histogram(df_demographics,x="Year",y = ['Grand Total','Grand Total Men','Grand Total Women'],text_auto=True,barmode='group')
fig_gender_bar.update_layout(title_text = 'Analysis of Stanford Undergraduate Admissions from 1980-2007: Gender Breakdown', #Title of the Plot
legend_title = 'Legend',
xaxis_title = 'Year', #x-axis label
yaxis_title = 'Number of Students', #y-axis label
barmode='group',
bargap=0.01, #Gap between bars of adjacent location
bargroupgap=0.01) #Gap between bars of the same location coordinates
fig_gender_bar.show()
# Analysis of Gender Breakdown from 1980 - 2007
fig_gender_line = px.line(df_demographics, x='Year', y=['Grand Total','Grand Total Men','Grand Total Women'], markers=True)
fig_gender_line.update_layout(autotypenumbers='convert types', # Updates the values from the dataframe from type object to numeric values
title_text = 'Analysis of Stanford Undergraduate Admissions from 1980-2007: Gender Breakdown', #Title of the Plot
legend_title = 'Legend',
xaxis_title = 'Year', #x-axis label
yaxis_title = 'Number of Students') #y-axis label # Updates the values from the dataframe from type object to numeric values
fig_gender_line.show()
When analyzing the enrollment of men to women over the years there is a steady increase in the enrollment of women from 1980 to 2000. In 1995 the number of women enrolled at Stanford surpassed the number of men enrolled in the institution for the first time in over a decade and continued to increase until 2000 where the number of women enrolled in Stanford peaked and started to decrease from there.
There are alot of factors that aren't taken into account within this study, but this data suggests that affirmitive action could have been a factor that positively impacted the number of women enrolled in this institution.
# Analysis of Gender Breakdown from 2010-2021
fig_gender_bar2 = px.histogram(df_demographics2,x='Year',y = ['Grand Total','Grand Total Men','Grand Total Women'],text_auto=True,barmode='group')
fig_gender_bar2.update_layout(title_text = 'Analysis of Stanford Undergraduate Admissions from 2010-2021: Gender Breakdown', #Title of the Plot
xaxis_title = 'Year', #x-axis label
yaxis_title = 'Number of Students', #y-axis label
legend_title = 'Legend',
barmode='group',
bargap=0.01, #Gap between bars of adjacent location
bargroupgap=0.01) #Gap between bars of the same location coordinates
fig_gender_bar2.show()
# Analysis of Gender Breakdown from 2010-2021
fig_gender_line2 = px.line(df_demographics2, x='Year', y=['Grand Total','Grand Total Men','Grand Total Women'], markers=True)
fig_gender_line2.update_layout(autotypenumbers='convert types', # Updates the values from the dataframe from type object to numeric values
title_text = 'Analysis of Stanford Undergraduate Admissions from 2010-2021: Gender Breakdown', #Title of the Plot
legend_title = 'Legend',
xaxis_title = 'Year', #x-axis label
yaxis_title = 'Number of Students') #y-axis label # Updates the values from the dataframe from type object to numeric values
fig_gender_line2.show()
From 2010 to 2021 the amount of men and women enrolled at Stanford remained relatively consistent with only an anomoly showing in 2020 possibly due to the pandemic and the effects of Covid 19 on our society.
# Analysis of Race/Ethinicty Breakdown from 1980-2007
fig_race = px.histogram(df_demographics,x="Year",y = ['Grand Total','White Non-Hispanic Total','Black Non-Hispanic Total','Hispanic Total','Asian or Pacific Islander Total','American Indian or Alaska Native Total','Race/Ethnicity Unknown Total','Nonresident Alien Total'],text_auto=True,barmode='group')
fig_race.update_layout(title_text = 'Analysis of Stanford Undergraduate Admissions from 1980-2007- Race/Ethnicity Breakdown', #Title of the Plot
legend_title = 'Legend',
xaxis_title = 'Year', #x-axis label
yaxis_title = 'Number of Applicants', #y-axis label
barmode='group',
bargap=0.01, #Gap between bars of adjacent location
bargroupgap=0.01) #Gap between bars of the same location coordinates
fig_race.show()
# Analysis of Race/Ethinicty Breakdown from 1980-2007
fig_race_line = px.line(df_demographics, x='Year', y = ['Grand Total','White Non-Hispanic Total','Black Non-Hispanic Total','Hispanic Total','Asian or Pacific Islander Total','American Indian or Alaska Native Total','Race/Ethnicity Unknown Total','Nonresident Alien Total'], markers=True)
fig_race_line.update_layout(autotypenumbers='convert types', # Updates the values from the dataframe from type object to numeric values
title_text = 'Analysis of Stanford Undergraduate Admissions from 1980-2007: Race/Ethnicity Breakdown', #Title of the Plot
legend_title = 'Legend',
xaxis_title = 'Year', #x-axis label
yaxis_title = 'Number of Applicants') #y-axis label # Updates the values from the dataframe from type object to numeric values
fig_race_line.show()
When analyzing the break down of race and ethnicity of the undergraduate population at Stanford from 1980 to 2007 the results show that the two races with significant changes were the White Non-Hispanic total and the Asian or Pacific Islander total. All other races/ethnicities stayed roughly the same over the years.
There are alot of factors that aren't taken into account within this study such as the possible influence of an increase in the Asian demographic in the United States or the number of applicants from each demographic recieved, but this data suggests that affirmitive action could have been a factor that positively impacted the number of Asian and Pacific Islander total at this institution.
# Analysis of Race/Ethinicty Breakdown from 2010-2021
fig_race2 = px.histogram(df_demographics2,x="Year",y = ['Grand Total','American Indian or Alaska Native Total','Asian Total ','Black or African American Total','Hispanic Total','Native Hawaiian or Other Pacific Islander Total','White Total','Two or More Races Total','Race/Ethnicity Unknown Total','Nonresident Alien Total'],text_auto=True,barmode='group')
fig_race2.update_layout(title_text = 'Analysis of Stanford Undergraduate Admissions from 2010-2021: Race/Ethnicity Breakdown', #Title of the Plot
xaxis_title = 'Year', #x-axis label
yaxis_title = 'Number of Applicants', #y-axis label
legend_title = 'Legend',
barmode='group',
bargap=0.01, #Gap between bars of adjacent location
bargroupgap=0.01) #Gap between bars of the same location coordinates
fig_race2.show()
# Analysis of Race/Ethinicty Breakdown from 2010-2021
fig_race_line = px.line(df_demographics2, x='Year', y = ['Grand Total','American Indian or Alaska Native Total','Asian Total ','Black or African American Total','Hispanic Total','Native Hawaiian or Other Pacific Islander Total','White Total','Two or More Races Total','Race/Ethnicity Unknown Total','Nonresident Alien Total'], markers=True)
fig_race_line.update_layout(autotypenumbers='convert types', # Updates the values from the dataframe from type object to numeric values
title_text = 'Analysis of Stanford Undergraduate Admissions from 2010-2021: Race/Ethnicity Breakdown', #Title of the Plot
xaxis_title = 'Year', #x-axis label
legend_title = 'Legend',
yaxis_title = 'Number of Applicants') #y-axis label # Updates the values from the dataframe from type object to numeric values
fig_race_line.show()
When analyzing the break down of race and ethnicity of the undergraduate population at Stanford from 2010 to 2021 the results show that there aren't significant changes in the number of students in each race/ethnicity.
# Analysis of Race/Ethinicty Percentages at Stanford 1980 Compared to U.S Populations
# Pie Chart of Race Percentages at Stanford 1980
fig_race_1980_Stanford = px.pie(df_demographics, values=[df_demographics['White Non-Hispanic Total'].values[0],df_demographics['Black Non-Hispanic Total'].values[0],df_demographics['Hispanic Total'].values[0],df_demographics['Asian or Pacific Islander Total'].values[0],df_demographics['American Indian or Alaska Native Total'].values[0],df_demographics['Race/Ethnicity Unknown Total'].values[0],df_demographics['Nonresident Alien Total'].values[0]],
names=['White','Black','Hispanic','Asian or Pacific Islander','American Indian or Alaska Native','Race/Ethnicity Unknown','Nonresident Alien Total'],)
fig_race_1980_Stanford.update_traces(textposition='inside', textinfo='percent+label',title_text = '1980 Stanford Racial/Ethnic Demographics of Undergraduate Admissions')
fig_race_1980_Stanford.show()
# Pie Chart of Race Percentages of U.S. Population
# Initialie Data of Lists
US_population1980 = { 'Race': ['White', 'Black','American Indian or Alaska Native', 'Asian and Pacific Islander','Other Races','Hispanic'],
'Percent': ['83.1','11.7','0.6','1.5','3.0','6.4']} # Data for the U.S Population came from the U.S Department of Commerce Buereau of the Census
df_US_population1980 = pd.DataFrame(US_population1980)
# Pie Chart of Race Percentages of U.S Population
fig_race_population1980 = px.pie(df_US_population1980, values= 'Percent',
names='Race')
fig_race_population1980.update_traces(textposition='inside', textinfo='percent+label',title_text = '1980 Racial/Ethnic Demographics of the United States Population')
fig_race_population1980.show()
When analyzing the racial/ethnic percentage breakdown of the Stanford demographic compared to the racial/ethnic demographic of the United States population in 1980, the data shows:
# Analysis of Race/Ethinicty Percentages at Stanford 2010 Compared to U.S Populations
# Pie Chart of Race Percentages at Stanford 2010
fig_race_2010_Stanford = px.pie(df_demographics2, values=[df_demographics2['White Total'].values[0],df_demographics2['Black or African American Total'].values[0],df_demographics2['Hispanic Total'].values[0],df_demographics2['Asian Total '].values[0],df_demographics2['American Indian or Alaska Native Total'].values[0],df_demographics2['Native Hawaiian or Other Pacific Islander Total'].values[0],df_demographics2['Two or More Races Total'].values[0],df_demographics2['Race/Ethnicity Unknown Total'].values[0],df_demographics2['Nonresident Alien Total'].values[0]],
names=['White','Black or African American','Hispanic','Asian','American Indian or Alaska Native','Native Hawaiian or Other Pacific Islander Total','Two or More Races Total','Race/Ethnicity Unknown','Nonresident Alien'],)
fig_race_2010_Stanford.update_traces(textposition='inside', textinfo='percent+label',title_text = '2010 Stanford Racial Demographics of Undergraduate Admissions')
fig_race_2010_Stanford.show()
# Pie Chart of Race Percentages of U.S. Population
# Initialie Data of Lists
US_population2010 = { 'Race': ['White', 'Black or African American','American Indian or Alaska Native', 'Asian','Native Hawaiian and Other Pacific Islander','Hispanic/Latino','Multiracial'],
'Percent': ['63.8','12.3','0.7','4.8','0.2','16.4','1.8']}
df_US_population2010= pd.DataFrame(US_population2010)
# Pie Chart of Race Percentages of U.S Population
fig_race_population2010 = px.pie(df_US_population2010, values= 'Percent',
names='Race')
fig_race_population2010.update_traces(textposition='inside', textinfo='percent+label',title_text = '2010 Racial Demographics of the United States Population')
fig_race_population2010.show()
When analyzing the racial/ethnic percentage breakdown of the Stanford demographic compared to the racial/ethnic demographic of the United States population in 2010, the data shows:
In summary, this comprehensive study explored the profound impact of affirmative action, or its absence, on Stanford University's demographics spanning the years from 1980 to 2021. As of June 2023, with the Supreme Court ruling against race-conscious admission practices in higher education, the findings of this analysis become even more pertinent for understanding the evolving landscape of diversity and equal opportunities in academia.
The study's key findings revealed significant shifts in gender and racial demographics over the decades. Initially, we observed a steady increase in female enrollment at Stanford, with women surpassing men in 1995, possibly indicative of affirmative action's influence in promoting gender diversity. However, the subsequent decline in female enrollment after 2000 underscores the need for nuanced analysis that considers various contributing factors beyond policy changes.
Furthermore, when comparing Stanford's demographic percentages to the U.S. population in both 1980 and 2010, stark disparities emerge, notably in the overrepresentation of Asians and the underrepresentation of Blacks at the university. Although it is vital to acknowledge the influence of external factors, such as demographic shifts and application rates, on these changes, these findings underscore the persistent challenges of achieving equitable representation and equal opportunities in higher education.
In light of these results, this study contributes to a broader understanding of the complexities surrounding affirmative action and its role in shaping university demographics. It prompts critical reflection on the need for comprehensive policies that address systemic disparities and promote inclusive education. As the landscape of higher education continues to evolve, this analysis serves as a valuable reference point for ongoing discussions and actions aimed at fostering diversity, equality, and access within our academic institutions.